Initial state prediction in planning

Krivic, Senka and Cashmore, Michael and Ridder, Bram and Magazzeni, Daniele and Szedmak, Sandor and Piater, Justus; (2017) Initial state prediction in planning. In: The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning - Technical Report. AAAI Press, Menlo Park, US-CA.. ISBN 9781577357865

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Abstract

While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multi- graph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.

ORCID iDs

Krivic, Senka, Cashmore, Michael ORCID logoORCID: https://orcid.org/0000-0002-8334-4348, Ridder, Bram, Magazzeni, Daniele, Szedmak, Sandor and Piater, Justus;
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